Marine fish traits follow fast-slow continuum across oceans

被引:55
作者
Beukhof, Esther [1 ]
Frelat, Romain [2 ]
Pecuchet, Laurene [1 ,3 ]
Maureaud, Aurore [1 ]
Dencker, Tim Spaanheden [1 ]
Solmundsson, Jon [4 ]
Punzon, Antonio [5 ]
Primicerio, Raul [3 ]
Hidalgo, Manuel [6 ]
Moellmann, Christian [2 ]
Lindegren, Martin [1 ]
机构
[1] Tech Univ Denmark, Ctr Ocean Life, Natl Inst Aquat Resources DTU Aqua, Lyngby, Denmark
[2] Univ Hamburg, Inst Marine Ecosyst & Fisheries Sci, Ctr Earth Syst Res & Sustainabil CEN, Hamburg, Germany
[3] UiT Arctic Univ Norway, Norwegian Coll Fishery Sci, Tromso, Norway
[4] Marine & Freshwater Res Inst, Reykjavik, Iceland
[5] Inst Espanol Oceanog, Ctr Oceanog Santander, Santander, Spain
[6] Inst Espanol Oceanog, Ctr Oceanog Balears, Ecosyst Oceanog Grp GRECO, Palma De Mallorca, Spain
基金
欧盟地平线“2020”;
关键词
LIFE-HISTORY STRATEGIES; CLIMATE-CHANGE; COMMUNITY; PATTERNS; SIZE; RESPONSES; GROWTH; TEMPERATURE; 4TH-CORNER; INDICATORS;
D O I
10.1038/s41598-019-53998-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A fundamental challenge in ecology is to understand why species are found where they are and predict where they are likely to occur in the future. Trait-based approaches may provide such understanding, because it is the traits and adaptations of species that determine which environments they can inhabit. It is therefore important to identify key traits that determine species distributions and investigate how these traits relate to the environment. Based on scientific bottom-trawl surveys of marine fish abundances and traits of >1,200 species, we investigate trait-environment relationships and project the trait composition of marine fish communities across the continental shelf seas of the Northern hemisphere. We show that traits related to growth, maturation and lifespan respond most strongly to the environment. This is reflected by a pronounced "fast-slow continuum" of fish life-histories, revealing that traits vary with temperature at large spatial scales, but also with depth and seasonality at more local scales. Our findings provide insight into the structure of marine fish communities and suggest that global warming will favour an expansion of fast-living species. Knowledge of the global and local drivers of trait distributions can thus be used to predict future responses of fish communities to environmental change.
引用
收藏
页数:9
相关论文
共 59 条
  • [1] [Anonymous], 2005, Ecosystems and human wellbeing: synthesis, DOI DOI 10.1196/ANNALS.1439.003
  • [2] Warming temperatures and smaller body sizes: synchronous changes in growth of North Sea fishes
    Baudron, Alan R.
    Needle, Coby L.
    Rijnsdorp, Adriaan D.
    Marshall, C. Tara
    [J]. GLOBAL CHANGE BIOLOGY, 2014, 20 (04) : 1023 - 1031
  • [3] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING
    BENJAMINI, Y
    HOCHBERG, Y
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) : 289 - 300
  • [4] Beukhof E., 2019, **DATA OBJECT**, DOI 10.1594/PANGAEA.900866
  • [5] Spatio-temporal variation in marine fish traits reveals community-wide responses to environmental change
    Beukhof, Esther
    Dencker, Tim S.
    Pecuchet, Laurene
    Lindegren, Martin
    [J]. MARINE ECOLOGY PROGRESS SERIES, 2019, 610 : 205 - 222
  • [6] Impact of fishing on size composition and diversity of demersal fish communities
    Bianchi, G
    Gislason, H
    Graham, K
    Hill, L
    Jin, X
    Koranteng, K
    Manickchand-Heileman, S
    Payá, I
    Sainsbury, K
    Sanchez, F
    Zwanenburg, K
    [J]. ICES JOURNAL OF MARINE SCIENCE, 2000, 57 (03) : 558 - 571
  • [7] Do climate and fishing influence size-based indicators of Celtic Sea fish community structure?
    Blanchard, JL
    Dulvy, NK
    Jennings, S
    Ellis, JR
    Pinnegar, JK
    Tidd, A
    Kell, LT
    [J]. ICES JOURNAL OF MARINE SCIENCE, 2005, 62 (03) : 405 - 411
  • [8] Fishing Impacts on Food Webs: Multiple Working Hypotheses
    Branch, Trevor A.
    [J]. FISHERIES, 2015, 40 (08) : 373 - 375
  • [9] Random forests
    Breiman, L
    [J]. MACHINE LEARNING, 2001, 45 (01) : 5 - 32
  • [10] Brown JH, 2004, ECOLOGY, V85, P1771, DOI 10.1890/03-9000